The importance of Geospatial Data in Digital Twins

Leveraging a Geospatial Digital Twin to assist when making business decisions can present a significant cost-cutting opportunity. The ability to predict changes and make alterations directly to the digital twin model can give you direct insight into how these modifications will impact the real-world environment. For example, it is much cheaper to replace assets that are at risk of failure in a planned and controlled way – especially if replacing several assets at once – rather than waiting for one to fail and replacing just that asset as a reactive ‘emergency response’ mode. 

Predictive modelling can also lead to life saving solutions. When mitigating natural disasters such as flooding, fires or even avalanches; a geospatial digital twin can help you identify the areas most at risk and respond accordingly with preventative measures such as building flood defences or increasing network capacity.

Digital Twin technology is well suited to be implemented as part of your maintenance schedules, with live modelling giving you powerful insight into weak points or other issues in a network or built environment.  

Digital Twins are intrinsically linked to the concept of Smart Cities. We can see National Mapping Agencies, such as SDFI in Denmark, are leading the way in the predictive modelling capabilities of Geospatial Digital Twins by using live models of built environments to accurately plan the optimum placement of solar panels in their cities. Geospatial digital twins of urban environments can have a tangible human benefit too, giving you the ability to factor in health and wellbeing into decisions, based on live data. With applications ranging from locating the optimum spots for public facilities or understanding areas of increased pollution, the human benefit is clear. 

Using solar panels as an example, you would use the building data to analyse roof size, orientation, and shading, as well as electricity network data to determine the capacity to carry the generated electricity, plus the demographics of the area to predict electricity demands such as electric car usage or home heating type. 

Another example could be the use of live feeds of traffic volumes, traffic light patterns and hospital capacity, to optimally route ambulances to an incident and then back to the most suitable hospital – those decisions may change constantly based on the traffic and hospital data.

It is key that the data driving your Geospatial Digital Twin is of a consistent and authoritative standard. This is especially important when considering the interoperability of data, which is a driving force behind the Geospatial Digital Twins being used in complex environments (urban areas) where there are multiple data siloes. As an example, in an urban environment, there may be underground asset data held by utilities organisations, and street level network data held by the local governments/municipalities.

These two sources, without any standardisation, would present considerable data siloes.

By storing and maintaining this data in separate databases, you can create challenges with integration, reducing your ability to create an accurate model and back business decisions with data. By introducing data standardisation, you can begin to make data interoperable and ingest it into your Geospatial Digital Twin. 

By using industry standards in your data management processes and throughout the lifecycle of your Digital Twin, you can be sure the models are of the quality and consistency required to allow you to make informed and accurate business decisions. Standards also create opportunities to integrate with wider data sets or contribute to data-sharing platforms and initiatives to bolster the representation of the real-world environment by allowing easy consumption and integration of the data feeds. 

Contrary to many expectations, a Geospatial Digital Twin can be represented by 2D or 3D data. Indeed, it is important when constructing your Digital Twin to consider how important using 3D data is for the purpose, or if 2D models are suitable. In many applications, the development of a fully 3-dimensional twin is a costly, but unnecessary overhead. Using a technology solution that leverages both 2D and 3D data and gives you the option to cross-reference your 2D or 3D Digital Twin with other 2D or 3D data sets held outside of the twin.

In other instances, the reverse can be true. A 2D model may seem adequate but creating a full 3D Geospatial Digital Twin can unlock the true potential of the data you have at your disposal. In the case of a utilities network model, this may be as simple as a fully realised 3D representation of your pipe system - giving you visual insight into the internal conditions of the pipes.

Digital Twins require an elevated level of detail to ensure they match operational reality, and any generalisation to your data set would mean that you are working with a more traditional data model. Working with an elevated level of detail, however, leads to several specific challenges, ranging from the need for a validated and precise foundation model, to the maintenance of accurate recording devices for live updates. To alleviate these challenges, it is important to ensure the detailed information you ingest is validated and fit for purpose, reducing the potential for mistakes down the line. 

Once started, the continuous integration of data into a Geospatial Digital Twin, requires a strong sense of data discipline. Quality requirements must be enforced on the data fed into the model and this is a challenge that needs consideration and investment. The process of guaranteeing valid and accurate data is integrated can be made easier during the initial stages of creating a Geospatial Digital Twin, simply by ensuring your foundation model is as accurate to the real-world environment as possible. 

The adage, “walk before you can run” is never truer than in the case of creating this foundation level of data. To ensure that any new data you ingest is of value, it is important to begin with a foundational framework of data you can trust. Without this, you cannot begin to import and integrate live data without running the risk of the model growing further and further away from operational reality.

To reach a point where your foundational framework is fit for purpose and ready for additional data, you must have a strong handle on the IDs of your features; this will help to ensure that the sensor readings are associated with the appropriate asset and remain so even as they are updated. Assets with the wrong or changing identifiers will inevitably lead to the incorrect features being updated, fundamentally undermining the value of a Geospatial Digital Twin. At 1Spatial, we employ Location Master Data Management as a key part of this process, enabling you to manage, validate, connect, or combine your data from multiple sources and systems, all whilst having confidence in the lifecycle of the identifiers of any feature. 

By validating the accuracy of data, costly overheads associated with inaccuracies are reduced. These costs would be exacerbated by the continual updates associated with a Geospatial Digital Twin in real time. Our rules-based approach to checking data automates these processes, embedding validation and data management into the process of data integration, giving you instant confidence in the validity of your 2D, 2.5D and 3D data.